NVIDIA

If hardware manufacturers want to keep their firmware crippling a secret, perhaps they shouldn’t mess with Linux users? We figure if you’re using Linux you’re quite a bit more likely than the average Windows user to crack something open and see what’s hidden inside. And so we get to the story of how [Gnif] figured out that the NVIDIA GTX690 can be hacked to perform like the Quadro K5000. The thing is, the latter costs nearly $800 more than the former!

[Gnif] wanted the card for gaming and to support multiple monitors. It has no problem driving up to three screens under Windows. But the Linux drivers only allow this on the professional counterpart to the GTX690, the Quadro K5000. It turns out that the card responds to a device ID as assigned by a series of analog values. These can be tweaked by swapping, yanking, or adding resistors in just the right places. As with that Agilent multimeter unlock of his which we saw a few days ago, he somehow managed to figure out the secret sauce that unlocks the power hidden in this card.

Powerful graphics cards are pretty affordable these days. Even though we rarely do high-end gaming on our daily machine we still have a GeForce 9800 GT. That goes to waste on a machine used mainly to publish posts and write code for microcontrollers. But perhaps we can put the GPU to good use when it comes compile time. The KGPU package enlists your graphics card to help the kernel do some heavy lifting.

This won’t work for just any GPU. The technique uses CUDA, which is a parallel computing package for NVIDIA hardware. But don’t let lack of hardware keep you from checking it out. [Weibin Sun] is one of the researchers behind the technique. He posted a whitepaper (PDF) on the topic over at his website.

If you ever need to manipulate images really fast, or just want to make some pretty fractals, [Reuben] has just what you need. He developed a neat command line tool to send code to a graphics card and generate images using pixel shaders. Opposed to making these images with a CPU, a GPU processes every pixel in parallel, making image processing much faster.

All the GPU coding is done by writing a bit of code in GLSL. [Reuben]’s command line utility takes that code, sends it to the graphics card, and returns the image calculated by the GPU. It’s very simple for to make pretty Mandebrolt set images and sine wave interference this way, but [Reuben]’s project can do much more than that. By sending an image to the GPU and performing a few operations, [Reuben] can do very fast edge detection and other algorithmic processing on pre-existing images.

So far, [Reuben] has tested his software with a few NVIDIA graphics cards under Windows and Linux, although it should work with any graphics card with pixel shaders.

Although [Reuben] is sending code to his GPU, it’s not quite on the level of the NVIDIA CUDA parallel computing platform; [Reuben] is only working with images. Cleverly written software could get around that, though. Still, even if [Reuben]’s project is only used for image processing, it’s still much faster than any CPU-bound method.

Solder connections on processors seem to be a very common failure point in modern electronics. Consider the Red Ring of Death (RRoD) on Xbox 360 or the Yellow Light of Death (YLoD) on PlayStation 3. This time around the problem is a malfunctioning Nvidia GPU on an HP Pavilion TX2000 laptop. The video is sometimes a jumbled mess and other times there’s no video at all. If the hardware is older, and the alternative to fixing it is to throw it away, you should try to reflow the solder connections on the chip.

This method uses a heat gun, which we’ve seen repair PCBs in the past. The goal here is to be much less destructive and that’s why the first step is to test out how well your heat gun will melt the solder. Place a chunk of solder on a penny, hold the heat gun one inch above it and record how long it takes the solder to flow. Once you have the timing right, mask off the motherboard (already removed from the case) so that just the chip in question is accessible. Reflow with the same spacing and timing as you did during the penny test. Hopefully once things cool down you’ll have a working laptop or gaming console again.

Recently, research students at Georgia Tech released a report outlining the dangers that GPUs pose to the current state of password security. There are a number of ways to crack a password, all with their different pros and cons, but when it comes down to it, the limiting factor in all of these methods is processing complexity. The more operations that need to be run, the longer it takes, and the less useful each tool is for cracking passwords. In the past, most recommendations for password security revolved around making sure your password wasn’t something predictable, such as “password” or your birthday. With today’s (and tomorrows) GPUs, this may no longer be enough.

The article then goes on to theorize why we have not seen more complaints. They say that failures of these type usually follow a bell curve distributed over the time domain and we are only on the initial up-slope. This is probably due to the different use patterns of the users. For example, people with laptops are turning their computers on and off more than desktop users, thus facilitating the heat cycling’s effect. They suggest the quick fix as more fanning, but eventually NVIDIA will have to do something about this.